Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging
Bibliographic record
Abstract
Advances in autonomous vehicle (AV) technology promise substantial gains in safety and operational efficiency; nonetheless, frequent lane changes and merging maneuvers remain critical safety challenges that impede smooth traffic flow. This paper proposes the Minimizing Lane Change Algorithm (MLCA), a finite-state-machine controller that defers non-safety-critical lane changes to maintain lane stability. We evaluated MLCA through 100 microscopic traffic simulations on the SUMO platform, executed on an Intel Core i5-8250U processor. Compared to the LC2017 and MOBIL models, MLCA achieved a 35% reduction in lane-change events and a 28% decrease in collision occurrences across diverse traffic densities and roadway geometries. These findings confirm MLCA's efficacy on commodity hardware and its compatibility with existing AV control architectures. Future research will assess MLCA within high-fidelity CARLA environments and investigate GPU-accelerated, distributed simulation frameworks to support large-scale validation and real-time deployment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".